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Revert hyperbolic trigonometric decompositions (llvm#3271)
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We should be using the `torch` path and handling decomposition in the
`math` dialect.
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@rsuderman
rsuderman authored May 3, 2024
1 parent 67d6a66 commit 321b844
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Showing 4 changed files with 64 additions and 176 deletions.
142 changes: 44 additions & 98 deletions lib/Conversion/TorchOnnxToTorch/DefaultDomainAtoF.cpp
View file
Original file line number Diff line number Diff line change
Expand Up @@ -300,29 +300,17 @@ void mlir::torch::onnx_c::populateDefaultDomainAtoF(
binder.op, resultType, operand);
return success();
});
patterns.onOp(
"Asinh", 9, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Value operand;
if (binder.tensorOperand(operand) ||
binder.tensorResultType(resultType))
return failure();

// log(x + sqrt(x**2 + 1))
Value square = rewriter.create<Torch::AtenSquareOp>(
binder.getLoc(), resultType, operand);
Value cstOne = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getI64IntegerAttr(1));
Value add0 = rewriter.create<Torch::AtenAddScalarOp>(
binder.getLoc(), resultType, square, cstOne, cstOne);
Value sqrt = rewriter.create<Torch::AtenSqrtOp>(binder.getLoc(),
resultType, add0);
Value add1 = rewriter.create<Torch::AtenAddTensorOp>(
binder.getLoc(), resultType, operand, sqrt, cstOne);
rewriter.replaceOpWithNewOp<Torch::AtenLogOp>(binder.op, resultType,
add1);
return success();
});
patterns.onOp("Asinh", 9,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Value operand;
if (binder.tensorOperand(operand) ||
binder.tensorResultType(resultType))
return failure();
rewriter.replaceOpWithNewOp<Torch::AtenAsinhOp>(
binder.op, resultType, operand);
return success();
});
patterns.onOp("Atan", 7,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Expand All @@ -334,33 +322,17 @@ void mlir::torch::onnx_c::populateDefaultDomainAtoF(
binder.op, resultType, operand);
return success();
});
patterns.onOp(
"Atanh", 9, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Value operand;
if (binder.tensorOperand(operand) ||
binder.tensorResultType(resultType))
return failure();

// 1/2 * log((1 + x) / (1 - x))
Value cstOne = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getI64IntegerAttr(1));
Value add = rewriter.create<Torch::AtenAddScalarOp>(
binder.getLoc(), resultType, operand, cstOne, cstOne);
Value neg = rewriter.create<Torch::AtenNegOp>(binder.getLoc(),
resultType, operand);
Value sub = rewriter.create<Torch::AtenAddScalarOp>(
binder.getLoc(), resultType, neg, cstOne, cstOne);
Value div = rewriter.create<Torch::AtenDivTensorOp>(
binder.getLoc(), resultType, add, sub);
Value log =
rewriter.create<Torch::AtenLogOp>(binder.getLoc(), resultType, div);
Value cstTwo = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getI64IntegerAttr(2));
rewriter.replaceOpWithNewOp<Torch::AtenDivScalarOp>(
binder.op, resultType, log, cstTwo);
return success();
});
patterns.onOp("Atanh", 9,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Value operand;
if (binder.tensorOperand(operand) ||
binder.tensorResultType(resultType))
return failure();
rewriter.replaceOpWithNewOp<Torch::AtenAtanhOp>(
binder.op, resultType, operand);
return success();
});
patterns.onOp("Acos", 7,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Expand All @@ -372,29 +344,17 @@ void mlir::torch::onnx_c::populateDefaultDomainAtoF(
binder.op, resultType, operand);
return success();
});
patterns.onOp(
"Acosh", 9, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Value operand;
if (binder.tensorOperand(operand) ||
binder.tensorResultType(resultType))
return failure();

// log(x + sqrt(x**2 - 1))
Value square = rewriter.create<Torch::AtenSquareOp>(
binder.getLoc(), resultType, operand);
Value cstOne = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getI64IntegerAttr(1));
Value sub = rewriter.create<Torch::AtenSubScalarOp>(
binder.getLoc(), resultType, square, cstOne, cstOne);
Value sqrt = rewriter.create<Torch::AtenSqrtOp>(binder.getLoc(),
resultType, sub);
Value add = rewriter.create<Torch::AtenAddTensorOp>(
binder.getLoc(), resultType, operand, sqrt, cstOne);
rewriter.replaceOpWithNewOp<Torch::AtenLogOp>(binder.op, resultType,
add);
return success();
});
patterns.onOp("Acosh", 9,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Value operand;
if (binder.tensorOperand(operand) ||
binder.tensorResultType(resultType))
return failure();
rewriter.replaceOpWithNewOp<Torch::AtenAcoshOp>(
binder.op, resultType, operand);
return success();
});
patterns.onOp("BatchNormalization", 15,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Expand Down Expand Up @@ -1490,31 +1450,17 @@ void mlir::torch::onnx_c::populateDefaultDomainAtoF(
binder.op, resultType, operand);
return success();
});
patterns.onOp(
"Cosh", 9, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Value operand;
if (binder.tensorOperand(operand) ||
binder.tensorResultType(resultType))
return failure();

// 1/2 * (exp(x) + exp(-x))
Value x = rewriter.create<Torch::AtenExpOp>(binder.getLoc(), resultType,
operand);
Value neg = rewriter.create<Torch::AtenNegOp>(binder.getLoc(),
resultType, operand);
Value y =
rewriter.create<Torch::AtenExpOp>(binder.getLoc(), resultType, neg);
Value cstOne = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getI64IntegerAttr(1));
Value z = rewriter.create<Torch::AtenAddTensorOp>(
binder.getLoc(), resultType, x, y, cstOne);
Value cstTwo = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getI64IntegerAttr(2));
rewriter.replaceOpWithNewOp<Torch::AtenDivScalarOp>(
binder.op, resultType, z, cstTwo);
return success();
});
patterns.onOp("Cosh", 9,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Value operand;
if (binder.tensorOperand(operand) ||
binder.tensorResultType(resultType))
return failure();
rewriter.replaceOpWithNewOp<Torch::AtenCoshOp>(
binder.op, resultType, operand);
return success();
});
patterns.onOp(
"CumSum", 11, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
Location loc = binder.getLoc();
Expand Down
35 changes: 11 additions & 24 deletions lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp
View file
Original file line number Diff line number Diff line change
Expand Up @@ -1439,31 +1439,18 @@ void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
return success();
});

patterns.onOp(
"Sinh", 9, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Value operand;
if (binder.tensorOperand(operand) ||
binder.tensorResultType(resultType))
return failure();
patterns.onOp("Sinh", 9,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Value operand;
if (binder.tensorOperand(operand) ||
binder.tensorResultType(resultType))
return failure();

// 1/2 * (exp(x) – exp(-x))
Value x = rewriter.create<Torch::AtenExpOp>(binder.getLoc(), resultType,
operand);
Value neg = rewriter.create<Torch::AtenNegOp>(binder.getLoc(),
resultType, operand);
Value y =
rewriter.create<Torch::AtenExpOp>(binder.getLoc(), resultType, neg);
Value cstOne = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getI64IntegerAttr(1));
Value z = rewriter.create<Torch::AtenSubTensorOp>(
binder.getLoc(), resultType, x, y, cstOne);
Value cstTwo = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getI64IntegerAttr(2));
rewriter.replaceOpWithNewOp<Torch::AtenDivScalarOp>(
binder.op, resultType, z, cstTwo);
return success();
});
rewriter.replaceOpWithNewOp<Torch::AtenSinhOp>(
binder.op, resultType, operand);
return success();
});

// split with fixed-size parts
// Arguments:
Expand Down
53 changes: 7 additions & 46 deletions test/Conversion/TorchOnnxToTorch/simple_ops_a_to_f.mlir
View file
Original file line number Diff line number Diff line change
Expand Up @@ -201,14 +201,7 @@ func.func @test_atan(%arg0: !torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,

// CHECK-LABEL: @test_atanh
func.func @test_atanh(%arg0: !torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,5],f32> attributes {torch.onnx_meta.ir_version = 3 : si64, torch.onnx_meta.opset_version = 9 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
// CHECK: %[[C1:.*]] = torch.constant.int 1
// CHECK: %[[ADD:.*]] = torch.aten.add.Scalar %arg0, %[[C1]], %[[C1]] : !torch.vtensor<[3,4,5],f32>, !torch.int, !torch.int -> !torch.vtensor<[3,4,5],f32>
// CHECK: %[[NEG:.*]] = torch.aten.neg %arg0 : !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32>
// CHECK: %[[SUB:.*]] = torch.aten.add.Scalar %[[NEG]], %[[C1]], %[[C1]] : !torch.vtensor<[3,4,5],f32>, !torch.int, !torch.int -> !torch.vtensor<[3,4,5],f32>
// CHECK: %[[DIV:.*]] = torch.aten.div.Tensor %[[ADD]], %[[SUB]] : !torch.vtensor<[3,4,5],f32>, !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32>
// CHECK: %[[LOG:.*]] = torch.aten.log %[[DIV]] : !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32>
// CHECK: %[[C2:.*]] = torch.constant.int 2
// CHECK: torch.aten.div.Scalar %[[LOG]], %[[C2]] : !torch.vtensor<[3,4,5],f32>, !torch.int -> !torch.vtensor<[3,4,5],f32>
// CHECK: torch.aten.atanh %arg0 : !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32>
%0 = torch.operator "onnx.Atanh"(%arg0) : (!torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,5],f32>
return %0 : !torch.vtensor<[3,4,5],f32>
}
Expand Down Expand Up @@ -672,13 +665,7 @@ func.func @test_cos(%arg0: !torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,5

// CHECK-LABEL: @test_cosh_example
func.func @test_cosh_example(%arg0: !torch.vtensor<[3],f32>) -> !torch.vtensor<[3],f32> attributes {torch.onnx_meta.ir_version = 3 : si64, torch.onnx_meta.opset_version = 9 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
// CHECK: %[[X:.+]] = torch.aten.exp %arg0 : !torch.vtensor<[3],f32> -> !torch.vtensor<[3],f32>
// CHECK: %[[NEG:.+]] = torch.aten.neg %arg0 : !torch.vtensor<[3],f32> -> !torch.vtensor<[3],f32>
// CHECK: %[[Y:.+]] = torch.aten.exp %[[NEG]] : !torch.vtensor<[3],f32> -> !torch.vtensor<[3],f32>
// CHECK: %[[C1:.+]] = torch.constant.int 1
// CHECK: %[[ADD:.+]] = torch.aten.add.Tensor %[[X]], %[[Y]], %[[C1]] : !torch.vtensor<[3],f32>, !torch.vtensor<[3],f32>, !torch.int -> !torch.vtensor<[3],f32>
// CHECK: %[[C2:.+]] = torch.constant.int 2
// CHECK: torch.aten.div.Scalar %[[ADD]], %[[C2]] : !torch.vtensor<[3],f32>, !torch.int -> !torch.vtensor<[3],f32>
// CHECK: torch.aten.cosh %arg0 : !torch.vtensor<[3],f32> -> !torch.vtensor<[3],f32>
%0 = torch.operator "onnx.Cosh"(%arg0) : (!torch.vtensor<[3],f32>) -> !torch.vtensor<[3],f32>
return %0 : !torch.vtensor<[3],f32>
}
Expand All @@ -687,13 +674,7 @@ func.func @test_cosh_example(%arg0: !torch.vtensor<[3],f32>) -> !torch.vtensor<[

// CHECK-LABEL: @test_cosh
func.func @test_cosh(%arg0: !torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,5],f32> attributes {torch.onnx_meta.ir_version = 3 : si64, torch.onnx_meta.opset_version = 9 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
// CHECK: %[[X:.+]] = torch.aten.exp %arg0 : !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32>
// CHECK: %[[NEG:.+]] = torch.aten.neg %arg0 : !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32>
// CHECK: %[[Y:.+]] = torch.aten.exp %[[NEG]] : !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32>
// CHECK: %[[C1:.+]] = torch.constant.int 1
// CHECK: %[[ADD:.+]] = torch.aten.add.Tensor %[[X]], %[[Y]], %[[C1]] : !torch.vtensor<[3,4,5],f32>, !torch.vtensor<[3,4,5],f32>, !torch.int -> !torch.vtensor<[3,4,5],f32>
// CHECK: %[[C2:.+]] = torch.constant.int 2
// CHECK: torch.aten.div.Scalar %[[ADD]], %[[C2]] : !torch.vtensor<[3,4,5],f32>, !torch.int -> !torch.vtensor<[3,4,5],f32>
// CHECK: torch.aten.cosh %arg0 : !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32>
%0 = torch.operator "onnx.Cosh"(%arg0) : (!torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,5],f32>
return %0 : !torch.vtensor<[3,4,5],f32>
}
Expand All @@ -702,12 +683,7 @@ func.func @test_cosh(%arg0: !torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,

// CHECK-LABEL: @test_acosh_example
func.func @test_acosh_example(%arg0: !torch.vtensor<[3],f32>) -> !torch.vtensor<[3],f32> attributes {torch.onnx_meta.ir_version = 3 : si64, torch.onnx_meta.opset_version = 9 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
// CHECK: %[[SQUARE:.+]] = torch.aten.square %arg0 : !torch.vtensor<[3],f32> -> !torch.vtensor<[3],f32>
// CHECK: %[[C1:.+]] = torch.constant.int 1
// CHECK: %[[SUB:.+]] = torch.aten.sub.Scalar %[[SQUARE]], %[[C1]], %[[C1]] : !torch.vtensor<[3],f32>, !torch.int, !torch.int -> !torch.vtensor<[3],f32>
// CHECK: %[[SQRT:.+]] = torch.aten.sqrt %[[SUB]] : !torch.vtensor<[3],f32> -> !torch.vtensor<[3],f32>
// CHECK: %[[ADD:.+]] = torch.aten.add.Tensor %arg0, %[[SQRT]], %[[C1]] : !torch.vtensor<[3],f32>, !torch.vtensor<[3],f32>, !torch.int -> !torch.vtensor<[3],f32>
// CHECK: torch.aten.log %[[ADD]] : !torch.vtensor<[3],f32> -> !torch.vtensor<[3],f32>
// CHECK: torch.aten.acosh %arg0 : !torch.vtensor<[3],f32> -> !torch.vtensor<[3],f32>
%0 = torch.operator "onnx.Acosh"(%arg0) : (!torch.vtensor<[3],f32>) -> !torch.vtensor<[3],f32>
return %0 : !torch.vtensor<[3],f32>
}
Expand All @@ -716,12 +692,7 @@ func.func @test_acosh_example(%arg0: !torch.vtensor<[3],f32>) -> !torch.vtensor<

// CHECK-LABEL: @test_acosh
func.func @test_acosh(%arg0: !torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,5],f32> attributes {torch.onnx_meta.ir_version = 3 : si64, torch.onnx_meta.opset_version = 9 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
// CHECK: %[[SQUARE:.+]] = torch.aten.square %arg0 : !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32>
// CHECK: %[[C1:.+]] = torch.constant.int 1
// CHECK: %[[SUB:.+]] = torch.aten.sub.Scalar %[[SQUARE]], %[[C1]], %[[C1]] : !torch.vtensor<[3,4,5],f32>, !torch.int, !torch.int -> !torch.vtensor<[3,4,5],f32>
// CHECK: %[[SQRT:.+]] = torch.aten.sqrt %[[SUB]] : !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32>
// CHECK: %[[ADD:.+]] = torch.aten.add.Tensor %arg0, %[[SQRT]], %[[C1]] : !torch.vtensor<[3,4,5],f32>, !torch.vtensor<[3,4,5],f32>, !torch.int -> !torch.vtensor<[3,4,5],f32>
// CHECK: torch.aten.log %[[ADD]] : !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32>
// CHECK: torch.aten.acosh %arg0 : !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32>
%0 = torch.operator "onnx.Acosh"(%arg0) : (!torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,5],f32>
return %0 : !torch.vtensor<[3,4,5],f32>
}
Expand All @@ -748,12 +719,7 @@ func.func @test_asin(%arg0: !torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,

// CHECK-LABEL: @test_asinh_example
func.func @test_asinh_example(%arg0: !torch.vtensor<[3],f32>) -> !torch.vtensor<[3],f32> attributes {torch.onnx_meta.ir_version = 3 : si64, torch.onnx_meta.opset_version = 9 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
// CHECK: %[[SQUARE:.+]] = torch.aten.square %arg0 : !torch.vtensor<[3],f32> -> !torch.vtensor<[3],f32>
// CHECK: %[[C1:.+]] = torch.constant.int 1
// CHECK: %[[ADD:.+]] = torch.aten.add.Scalar %[[SQUARE]], %[[C1]], %[[C1]] : !torch.vtensor<[3],f32>, !torch.int, !torch.int -> !torch.vtensor<[3],f32>
// CHECK: %[[SQRT:.+]] = torch.aten.sqrt %[[ADD]] : !torch.vtensor<[3],f32> -> !torch.vtensor<[3],f32>
// CHECK: %[[ADD_0:.+]] = torch.aten.add.Tensor %arg0, %[[SQRT]], %[[C1]] : !torch.vtensor<[3],f32>, !torch.vtensor<[3],f32>, !torch.int -> !torch.vtensor<[3],f32>
// CHECK: torch.aten.log %[[ADD_0]] : !torch.vtensor<[3],f32> -> !torch.vtensor<[3],f32>
// CHECK: torch.aten.asinh %arg0 : !torch.vtensor<[3],f32> -> !torch.vtensor<[3],f32>
%0 = torch.operator "onnx.Asinh"(%arg0) : (!torch.vtensor<[3],f32>) -> !torch.vtensor<[3],f32>
return %0 : !torch.vtensor<[3],f32>
}
Expand All @@ -762,12 +728,7 @@ func.func @test_asinh_example(%arg0: !torch.vtensor<[3],f32>) -> !torch.vtensor<

// CHECK-LABEL: @test_asinh
func.func @test_asinh(%arg0: !torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,5],f32> attributes {torch.onnx_meta.ir_version = 3 : si64, torch.onnx_meta.opset_version = 9 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
// CHECK: %[[SQUARE:.+]] = torch.aten.square %arg0 : !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32>
// CHECK: %[[C1:.+]] = torch.constant.int 1
// CHECK: %[[ADD:.+]] = torch.aten.add.Scalar %[[SQUARE]], %[[C1]], %[[C1]] : !torch.vtensor<[3,4,5],f32>, !torch.int, !torch.int -> !torch.vtensor<[3,4,5],f32>
// CHECK: %[[SQRT:.+]] = torch.aten.sqrt %[[ADD]] : !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32>
// CHECK: %[[ADD_0:.+]] = torch.aten.add.Tensor %arg0, %[[SQRT]], %[[C1]] : !torch.vtensor<[3,4,5],f32>, !torch.vtensor<[3,4,5],f32>, !torch.int -> !torch.vtensor<[3,4,5],f32>
// CHECK: torch.aten.log %[[ADD_0]] : !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32>
// CHECK: torch.aten.asinh %arg0 : !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32>
%0 = torch.operator "onnx.Asinh"(%arg0) : (!torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,5],f32>
return %0 : !torch.vtensor<[3,4,5],f32>
}
Expand Down
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